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Updated: Aug 13, 2026
Bacterial Detection & Identification Using Electrochemical Sensors
Published on: April 23, 2013
Construction of a colorimetric sensor array based on two novel carboxylated nitrogen heterocycle-copper coordinated
Ke Li1, Xuesong Wang1, Xixingchi Chen1
1Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, College of New Energy and Environment, Jilin University, Changchun 130021, PR China; Jilin Provincial Key Laboratory of Water Resources and Water Environment, College of New Energy and Environment, Jilin University, Changchun 130021, PR China.
Abstract:
Phenolic endocrine disruptors (PEDs) are widespread environmental contaminants that threaten reproductive health and metabolic functions. Accordingly, technologies enabling their simultaneous identification and detection are urgently needed, especially for structurally similar PEDs that often coexist. Laccase-like nanozymes, as environmentally friendly catalytic materials, offer advantages such as ease of preparation and high stability, and can catalyze the oxidation reactions of various substrates, including phenols. In this study, two novel carboxylated nitrogen heterocycle-copper coordinated laccase-like nanozymes (CuINA and Cu2Cl-4PDA) were synthesized from nicotinic acid derivatives by increasing the number of nitrogen atoms and modifying their relative positions. These nanozymes enhance the colorimetric reaction, exhibiting approximately threefold (CuINA) and twofold (Cu2Cl-4PDA) higher color intensity compared to CuNA. They also demonstrate excellent stability over a broad range of conditions (pH 4-11, 30-90 °C, 0-400 mM NaCl, and 7 days of storage). A colorimetric sensor array was subsequently constructed using four sensor units (CuINA+4-AP, Cu2Cl-4PDA+4-AP, CuINA+2,4-DP+4-AP, and Cu2Cl-4PDA+2,4-DP+4-AP). Combined with linear discriminant analysis, the array successfully identified five PEDs across a concentration range of 10-100 µM. Furthermore, it accurately differentiated mixed samples and demonstrated robust interference resistance, making it suitable for real water samples such as tap water, lake water, and domestic wastewater. Additionally, integration with machine learning enabled concentration-independent detection of five PEDs, overcoming the limitation of sensitivity to concentration fluctuations. Overall, this study provides a tool for rapid, simple, and reliable identification of PED types and concentration ranges during emergency response, advances the rational design of laccase-like nanozymes, and supports ecosystem protection and public health.
